Search

BACKGROUND: Both defects in mismatch repair (dMMR) and high microsatellite instability (MSI-H) have been recognised as crucial biomarkers that guide treatment strategies and disease management in colorectal cancer (CRC). As MMR and MSI tests are being widely conducted, an increasing number of MSI-H tumours have been identified in CRCs with mismatch repair proficiency (pMMR). The objective of this study was to assess the clinical features of patients with pMMR/MSI-H CRC and elucidate the underlying molecular mechanism in these cases. METHODS: From January 2015 to December 2018, 1684 cases of pMMR and 401 dMMR CRCs were enrolled. Of those patients, 93 pMMR/MSI-H were identified. The clinical phenotypes and prognosis were analysed. Frozen and paraffin-embedded tissue were available in 35 patients with pMMR/MSI-H, for which comprehensive genomic and transcriptomic analyses were performed. FINDINGS: In comparison to pMMR/MSS CRCs, pMMR/MSI-H CRCs exhibited significantly less tumour progression and better long-term prognosis. The pMMR/MSI-H cohorts displayed a higher presence of CD8+ T cells and NK cells when compared to the pMMR/MSS group. Mutational signature analysis revealed that nearly all samples exhibited deficiencies in MMR genes, and we also identified deleterious mutations in MSH3-K383fs. INTERPRETATION: This study revealed pMMR/MSI-H CRC as a distinct subgroup within CRC, which manifests diverse clinicopathological features and long-term prognostic outcomes. Distinct features in the tumour immune-microenvironment were observed in pMMR/MSI-H CRCs. Pathogenic deleterious mutations in MSH3-K383fs were frequently detected, suggesting another potential biomarker for identifying MSI-H. FUNDING: This work was supported by the Science and Technology Commission of Shanghai Municipality (20DZ1100101).

Data has become a high-value commodity in manufacturing. There is a growing realisation that the data-driven applications could become strong differentiators of manufacturing enterprises. To guide the developments in digitisation, a widely accepted framework is needed. In the absence of the universal framework, the components making a digital enterprise are captured in an example framework that is introduced in the paper. The adoption of new technology and software solutions has increased complexity of manufacturing systems. In addition, new product introductions have become more frequent and the demand more variable. A digital space enables optimisation and simulation of decisions before their realisation in the physical space. Predictive modelling with its time dimension is a valuable actor in the digital space. Three challenges of predictive modelling such as model complexity, model interpretability, and model reuse are identified in this paper. The coverage of each challenge in the literature is illustrated with the recently published papers. The main aspects of these challenges and the synthesis of the developments in digital manufacturing are articulated in the form of eight observations that could guide the future research. [ABSTRACT FROM AUTHOR]

Background: This paper demonstrates how available data can be explored and utilized to conclude generic patterns in the temporal changes in Human Milk (HM) composition. Methods: The temporal trajectories of selected human milk components (HMC-s) were described, in the first four months postpartum, by a primary model consisting of two phases: a short linear phase in the colostrum, triggered by the parturition; and a longer second phase, where the concentration of the component converges to a steady state. The model was fitted to data available in a recently published database of temporal HMC trajectories both at the levels of individual molecules (such as specific fatty acid, oligosaccharide, and mineral molecules) and molecule-groups (such as total protein, total fat). Results: The properties of the trajectories suggest that experimental designs should follow non-equidistant sampling times, with increasingly longer time intervals after the first week postpartum. A selected parameter, the final stationary level, of the primary model was then studied as a function of geographical location (secondary modelling). Conclusions: We found that the total variation of the concentration of specific HMC-s is dominantly due to the inherent biological differences between individual mothers and to less extent to the geographical location. [ABSTRACT FROM AUTHOR]

ABSTRACT Coral reef ecosystems support high fish biodiversity through ecological interactions with structural complexity across multiple spatial scales including coral colony architecture and the surrounding seascape structure. In an era where the complexity of coral reef ecosystems is being diminished, understanding the importance of structural characteristics beyond single focal patches has the potential to better inform actions for protecting, restoring or creating habitat for reef‐associated species. A seascape ecology approach was applied to explore the associations between multiple scales of seascape structure and fish assemblage response variables within a small (49.6 km2) offshore no‐take MPA, Sir Bu Nair Island Protected Area, in Sharjah, United Arab Emirates. Fish–seascape associations were modelled with single regression trees. Both in situ and remote sensing–derived variables produced the best models with highest contributions from coral cover, amount of hard‐bottom habitat type and structural complexity of the seafloor terrain. Fish species richness was significantly higher where coral cover exceeded 35%. The hard‐bottom areas with coral supported diverse assemblages dominated by carnivorous and omnivorous fishes. The Sir Bu Nair Island Protected Area provides a critical refuge for threatened and regionally overexploited species including those with low resilience to fishing. The ecological success of this protected area is key to safeguarding regional marine biodiversity and recovering fish populations to enhance food security. [ABSTRACT FROM AUTHOR]

Benthic species assemblages are groups of species that co-occur on the seafloor. Linking assemblages to physical environmental features allows for understanding and predicting their spatial distribution. Species identity and abundance are commonly quantified using a taxonomic approach to assess benthic diversity, yet functional traits that describe the behavior, life history, and morphology of a species may be equally or more important. Here, we investigate the biodiversity of five benthic species assemblages in relation to their habitat and environmental conditions in an Ecologically and Biologically Significant Area (EBSA) along Canada's east coast, using both a taxonomic approach and biological traits analysis. Random Forest regression was applied to map spatial patterns of functional and taxonomic diversity metrics, including richness, Shannon index, and Rao's quadratic entropy. We evaluate discrepancies between related taxonomic and trait measures, and the community-weighted mean of trait data was calculated to characterize each assemblage. Taxonomic and functional richness - representing the number of species and the species community volume in the trait space, respectively - showed similar spatial patterns. However, when considering diversity, which also accounts for the relative abundance and differences among species or traits, these patterns diverged. Taxonomically different assemblages exhibited similar trait compositions for two assemblages, indicating potential trait equivalencies, while one assemblage exhibited traits potentially indicating sensitivity to human activity. The taxonomic and functional metrics of richness and diversity were low close to the coast, which could be indicative of disturbance. Consideration of functional metrics can support spatial planning and prioritization for management and conservation efforts by assessing the sensitivity of traits to different stressors. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the performance of various prediction models in estimating the growth and morphological traits of pure Hair, Alpine × Hair F1 (AHF1), and Saanen × Hair F1 (SHF1) hybrid offspring at yearling age by employing early body measurement records from birth till 9th month combined with meteorological data, in an extensive natural pasture-based system. The study also included other factors such as sex, farm, doe and buck IDs, birth type, gestation length, age of the doe at birth etc. For this purpose, seven different machine learning algorithms—linear regression, artificial neural network (ANN), support vector machines (SVM), decision tree, random forest, extra gradient boosting (XGB) and ExtraTree – were applied to the data coming from 1530 goat offspring in Türkiye. Early predictions of growth and morphological traits at yearling age; such as live weight (LW), body length (BL), wither height (WH), rump height (RH), rump width (RW), leg circumference (LC), shinbone girth (SG), chest width (CW), chest girth (CG) and chest depth (CD) were performed by using birth date measurements only, up to month-3, month-6 and month-9 records. Satisfactory predictive performances were achieved once the records after 6th month were used. In extensive natural pasture-based systems, this approach may serve as an effective indirect selection method for breeders. Using month-9 records, the predictions were improved, where LW and BL were found with the highest performance in terms of coefficient of determination (R2 score of 0.81 ± 0.00) by ExtraTree. As one of the rarely applied machine learning models in animal studies, we have shown the capacity of this algorithm. Overall, the current study offers utilization of the meteorological data combined with animal records by machine learning models as an alternative decision-making tool for goat farming. [ABSTRACT FROM AUTHOR]

Global change is impacting biodiversity across all habitats on earth. New selection pressures from changing climatic conditions and other anthropogenic activities are creating heterogeneous ecological and evolutionary responses across many species' geographic ranges. Yet we currently lack standardised and reproducible tools to effectively predict the resulting patterns in species vulnerability to declines or range changes.We developed an informatic toolbox that integrates ecological, environmental and genomic data and analyses (environmental dissimilarity, species distribution models, landscape connectivity, neutral and adaptive genetic diversity, genotype‐environment associations and genomic offset) to estimate population vulnerability. In our toolbox, functions and data structures are coded in a standardised way so that it is applicable to any species or geographic region where appropriate data are available, for example individual or population sampling and genomic datasets (e.g. RAD‐seq, ddRAD‐seq, whole genome sequencing data) representing environmental variation across the species geographic range.To demonstrate multi‐species applicability, we apply our toolbox to three georeferenced genomic datasets for co‐occurring East African spiny reed frogs (Afrixalus fornasini, A. delicatus and A. sylvaticus) to predict their population vulnerability, as well as demonstrating that range loss projections based on adaptive variation can be accurately reproduced from a previous study using data for two European bat species (Myotis escalerai and M. crypticus).Our framework sets the stage for large scale, multi‐species genomic datasets to be leveraged in a novel climate change vulnerability framework to quantify intraspecific differences in genetic diversity, local adaptation, range shifts and population vulnerability based on exposure, sensitivity and landscape barriers. [ABSTRACT FROM AUTHOR]

Central serous chorioretinopathy (CSCR) is a retinal disease characterised by the accumulation of subretinal fluid, which often resolves spontaneously in acute cases. However, approximately one-third of patients experience recurrences that may cause severe and irreversible vision. This study aimed to identify parameters derived from optical coherence tomography (OCT) that are associated with CSCR recurrence. Our dataset included 5211 OCT scans from 344 eyes of 255 patients diagnosed with CSCR. 178 eyes were identified as recurrent, 109 as non-recurrent, and 57 were excluded. We extracted parameters using artificial intelligence algorithms based on U-Nets, convolutional kernels, and morphological operators. We applied inferential statistics to evaluate differences between the recurrent and non-recurrent groups, and we used a logistic regression predictive model, reporting the coefficients as a measure of biomarker importance. We identified nine predictive biomarkers for CSCR recurrence: age, intraretinal fluid, subretinal fluid, pigment epithelial detachments, choroidal vascularity index, integrity of photoreceptors and retinal pigment epithelium layer, choriocapillaris and choroidal stroma thickness, and thinning of the outer nuclear layer, and of the inner nuclear layer combined with the outer plexiform layer. These results could enable future developments in the automatic detection of CSCR recurrence, paving the way for translational medical applications. [ABSTRACT FROM AUTHOR]

This study explores the fabrication and characterisation of 3D-printed polylactic acid (PLA) scaffolds reinforced with calcium hydroxyapatite (cHAP) for bone tissue engineering applications. By varying the cHAP content, we aimed to enhance PLA scaffolds' mechanical and thermal properties, making them suitable for load-bearing biomedical applications. The results indicate that increasing cHAP content improves the tensile and compressive strength of the scaffolds, although it also increases brittleness. Notably, incorporating cHAP at 7.5% and 10% significantly enhances thermal stability and mechanical performance, with properties comparable to or exceeding those of human cancellous bone. Furthermore, this study integrates machine learning techniques to predict the mechanical properties of these composites, employing algorithms such as XGBoost and AdaBoost. The models demonstrated high predictive accuracy, with R2 scores of 0.9173 and 0.8772 for compressive and tensile strength, respectively. These findings highlight the potential of using data-driven approaches to optimise material properties autonomously, offering significant implications for developing custom-tailored scaffolds in bone tissue engineering and regenerative medicine. The study underscores the promise of PLA/cHAP composites as viable candidates for advanced biomedical applications, particularly in creating patient-specific implants with improved mechanical and thermal characteristics. [ABSTRACT FROM AUTHOR]

Past land-use reconstructions are a key tool for studying long-term human ecodynamics and addressing pressing questions about the origins and evolutionary dynamics of the Anthropocene. In particular, agricultural landcover reconstructions are vital for understanding long-term human-environment dynamics. Most past agricultural land-use models, however, rely heavily on modelling assumptions, make limited use of known archaeological site locations to constrain or inform their estimates and tend to be limited to general estimates of percentages of plant types within catchments around pollen trapping lakes. The lack of information outside catchment areas and low spatial resolution even within catchments constrain the utility of these models. To address this problem, we propose a new approach that combines archaeological predictive modelling with pollen-based agricultural landcover reconstructions to produce more accurate, spatially explicit past landcover estimates. Here, we present the results of a case study deploying the new approach to produce improved past landcover maps for a region in the Western Taurus Mountains, southwestern Turkey. The study area surrounds Sagalassos, an antique urban centre with a regional settlement history encompassing nine millennia. We produced five archaeological predictive models for the study region using the 'Locally Adaptive Models of Archaeological Potential' (LAMAP) method spanning the Hellenistic through Late Ottoman period. We then combined those predictive surfaces with 'Regional Estimates of VEgetation Abundance from Large Sites' (REVEALS) pollen landcover reconstructions for the same periods based on pollen from sediment cores extracted from three catchments within the study area. Lastly, we compared the resulting hybrid landcover models to the archaeological record using data not used to make the predictions. We found that the hybrid landcover model aligned very well with the known extents of agricultural land-use from the study area. These results indicate that the proposed approach is a viable way to combine pollen-based landcover models with archaeological data and produce more accurate, empirically-based landcover reconstructions reflecting real human activity in the past. [ABSTRACT FROM AUTHOR]

Aim: Shifting climates are reshaping ecosystems globally and are projected to intensify over the coming century. Understanding how biodiversity will respond to these shifts is crucial for developing effective climate adaptation measures. We generate predictive models built from long‐term data to hindcast historic fluctuations in small mammal abundances as they have responded to shifting rainfall and fire conditions. This data set serves as the basis for predicting historical variations (hindcasting) in small mammal abundances, allowing us to examine their responses to decadal changes in fire and rainfall conditions within our study landscape. Location: Australia (Victoria). Taxa: Small mammals (Mammalia). Time Period: 1970–2022. Methods: Small mammal abundance was surveyed at 36 long‐term trapping sites and modelled against coinciding fire history, vegetation productivity and rainfall using generalized additive mixed models. Six species were then used in predictive modelling against these variables for the decades preceding our monitoring programme (1970–2007). Results: All species abundances increased with higher rainfall. Time since fire was also an important variable in all but one species model, with species displaying varying responses to time since fire. Hindcasting predictions for small mammal abundances varied with some species showing marked declines over time. Clear trends emerged, indicating more volatile population fluctuations in response to intensified fire and rainfall extremes in the 21st century. This suggests that periods of higher rainfall and less frequent fire events in the decades preceding our monitoring period supported higher and more stable small mammal abundances. Conclusions: Native species show distinct sensitivity to the combined effects of drought and fire, which has occurred in recent times. Intensification of these drivers has caused increased volatility in small mammal abundances with low abundance extremes occurring more frequently. [ABSTRACT FROM AUTHOR]

Predicting video virality is the holy grail of marketing today. Previous video virality prediction research has relied upon two processes that may lead to incomplete data or incomplete analyses: non-subconscious data collection and self-reporting. This exploratory study evaluates the potential of using a physiological manifestation of emotions captured through facial expressions and skin conductance to predict video viewer engagement across the viewing experience. In the context of video virality and user-focused emotional response, an experiment with 64 subjects viewing 13 videos collected facial expression and galvanic skin response data during the entire viewing experience. XGBoost classifier was deployed using 42 collected features (physiological data and socio-behavioural responses). The selected classifier could predict user engagement with over 80% accuracy. In addition to socio-demographic data and behavioural features, the predictive model identified several facial expression-based features, including action units (mouth open, cheek raise, eye closure, lip raise, and smile), emotions (joy), engagement and positive valence along with head movement (attention) and arousal (GSR peaks) as the most impactful. This study confirms the predictive capability of physiological data and elevates the value and need to understand further the role of viewers' physiological and subconscious responses to video content across the viewing experience. [ABSTRACT FROM AUTHOR]

Aims/Background Acute radiation dermatitis is the most common complication of radiotherapy in patients with breast cancer, with mild severity relieved by symptomatic treatment and moderate-to-severe severity leading to compromised skin integrity and affecting the patient's quality of life. Therefore, this study aims to develop a prediction model for moderate-to-severe acute radiation dermatitis in patients with breast cancer to reduce its severity. Methods A retrospective analysis of 713 patients receiving radiotherapy for breast cancer at the Affiliated Cancer Hospital of Xinjiang Medical University from January 2019 to December 2023 was conducted, with January 2019 to December 2021 serving as the training group (497 patients) and January 2022 to December 2023 serving as the validation group (216 patients). Patients in the training group were classified as having mild (383 patients) or moderately severe (114 patients) acute radiation dermatitis. Binary logistic regression was used to analyze the independent effects on moderately severe acute radiation dermatitis in patients with breast cancer, and a predictive model of the bar-folding plot was constructed and validated. Results Univariable analysis revealed that age, body mass index, targeted therapy, oral tamoxifen use, hyperlipidemia, diabetes, positive regional lymph node metastasis, value-added index, and triple-negative breast cancer were factors influencing moderate-to-severe acute radiation dermatitis in patients with breast cancer. Multivariate analysis showed that body mass index, hyperlipidemia, diabetes, positive regional lymph node metastasis, and value-added index were independent influencing factors for moderate-to-severe acute radiation dermatitis in patients with breast cancer. A nomogram prediction model was constructed, and the area under the receiver operating characteristic curve of the model was 0.814 and 0.743 for internal and external validation, respectively. The calibration curve showed that the model predicted moderate-to-severe acute radiation dermatitis better, and the decision curve analysis curve showed that the model had a high clinical benefit. Conclusion This risk prediction model can predict moderate-to-severe acute radiation dermatitis in patients with breast cancer, and help clinical providers screen high-risk patients and reduce acute radiation dermatitis severity. [ABSTRACT FROM AUTHOR]

Background Materials & Methods Results Conclusion The goal of this research was to identify how early in psychotherapy a reliable prediction regarding the probability of premature termination, treatment failure or treatment success can be made, and which variables are most predictive. This research breaks new ground by including data on each clinician's historical outcomes (therapist effects) to explore to what degree predictions and decision rules might differ with therapist effectiveness.Data was drawn from a large online database of clients regularly completing self‐report outcome questionnaires. The sample included clinicians (n = 1020) treating clients (n = 68,690) with an intake score in the clinical range during a treatment episode of between 2 and 10 sessions. Variables available at the first session and later sessions were tested for predictive validity for status at subsequent sessions using general linear regression models and logistic regression models.The results suggest that therapy outcomes can be predicted as early as the second session with a good degree of accuracy, 65%. The analyses revealed that predictions made as early as the second session remain valid even for clients taking up to 10 sessions to complete treatment. Adding therapist effects to the predictive model provides a much more nuanced understanding of the results. By session two, therapists in the top quartile demonstrate effect sizes comparable to the effect sizes of bottom quartile therapists at session ten.The probability of client success, failure or early termination varies significantly depending on the clinician. Implications for training and supervision are discussed. [ABSTRACT FROM AUTHOR]

AbstractThrough the increasing use of supplementary cementitious materials, the properties of concrete have taken on increased significance in a design code. Using reliable prediction models based on a small data set for the mechanical properties and durability of concrete can reduce the number of trial batches and experiments needed to produce useful design data in the laboratory, reducing time as well as resources. In this study, we investigate how the properties of water penetration, chlorine resistance, and compressive strength can be predicted by polynomial regression (PR), random forest (RF) regression, and artificial neural networks (ANNs) based on the input values of density, workability, and the constituent amount of rice husk ash, cement, and nano SiO2. We vary the training data used and test the coefficient of determination (R2 score) on the remaining data as a test set to measure predictive capability. We show that RFs and ANNs outperform PR in all settings and have unambiguously extrapolating properties when hyperparameter optimization is designed for this purpose. Remarkably, we obtain R2 scores on the test data of 0.858 − 0.990 for RFs and 0.825 − 0.985 for ANNs. [ABSTRACT FROM AUTHOR]

This study presents the findings of a comprehensive geotechnical and seismic site investigation conducted at Otuasega Town located in Bayelsa State within the Niger Delta region of Nigeria. Subsurface exploration involved advancing 10 boreholes to 30 m depth using hollow stem auger drilling. Continuous disturbed and undisturbed soil sampling was performed at 1.5 m intervals for detailed geotechnical testing. Laboratory tests on the recovered soil samples established the index properties, classification, densities and consistency limits of the stratified deposits. The subsurface profile comprised alternating layers of clay, silt and sand typical of deltaic sediments, with the clay fractions exhibiting medium to high plasticity. Shear wave velocity (Vs) profiling using Multichannel Analysis of Surface Waves (WASW) techniques categorised the site predominantly as Site Class C and D based on international standards. The Standard Penetration Test (SPT) N-values ranged from 5 to 10, indicating soft normally consolidated clay conditions typical of the Niger Delta region. Predictive empirical models developed from the field and lab data showed strong correlations for estimating key geotechnical parameters such as SPT blow count, Vs and liquefaction resistance. Ground response analyses using the Vs and SPT data indicated significant site amplification potential, with peak ground accelerations up to 1.5 times the bedrock motion. Liquefaction analysis based on the empirical SPT-based methods revealed a high potential for liquefaction in the sandy layers, especially under strong earthquake shaking. The study characterized the complex sedimentology and provided baseline information for seismic microzonation and site-specific ground response analyses to advance understanding of geohazards in this delta environment. [ABSTRACT FROM AUTHOR]

It is widely accepted that loneliness is associated with health problems, but less is known about the predictors of loneliness. In this study, we constructed a model to predict individual risk of loneliness during adulthood. Data were from the prospective population-based FinHealth cohort study with 3444 participants (mean age 55.5 years, 53.4% women) who responded to a 81-item self-administered questionnaire and reported not to be lonely at baseline in 2017. The outcome was self-reported loneliness at follow-up in 2020. Predictive models were constructed using bootstrap enhanced LASSO regression (bolasso). The C-index from the final model including 11 predictors from the best bolasso -models varied between 0.65 (95% CI 0.61 to 0.70) and 0.71 (95% CI 0.67 to 0.75) the pooled C -index being 0.68 (95% CI 0.61 to 0.75). Although survey-based individualised prediction models for loneliness achieved a reasonable C-index, their predictive value was limited. High detection rates were associated with high false positive rates, while lower false positive rates were associated with low detection rates. These findings suggest that incident loneliness during adulthood. may be difficult to predict with standard survey data. • We constructed a survey -based model predicting individual level risk of loneliness. • The overall predictive performance of the model was reasonable. • However, high detection rate was accompanied with high false positive rate. • The model included variables related to social relations, but also to poor sleep, female sex and depressive symptoms. [ABSTRACT FROM AUTHOR]

A growing challenge in canola (Brassica napus L.) production globally is the management of aphid pests, particularly species that are resistant to insecticides. Aphid pests of canola damage plants through direct feeding and virus transmission, with turnip yellows virus being particularly economically damaging. Integrated Pest Management, a strategy now employed by many growers to reduce the risk of insecticide resistance, requires forward planning and monitoring. Improved risk predictions can be used to help growers limit insecticide spraying by targeting high‐risk regions and/or periods. Within Australia, autumnal aphid flights coincide with the critical risk period for virus infestations in canola. In this study, we used an extensive database accumulated from 6 years of surveys collected from more than 200 canola fields across southern Australia with supervised machine learning models to predict aphid movements in autumn‐early winter as a function of environmental factors. We found: (i) our models achieve very high predictive accuracy when validated on untrained data; (ii) aphid movements are influenced by a combination of daily temperature and wind regimes as well as 'green bridge' effects mediated by summer rainfall patterns; and (iii) higher aphid capture rates in sticky traps are correlated with a higher probability of the aphids being carriers of turnip yellows virus. Taken together these results suggest that growers can use the outputs from predictive models to forecast aphid outbreaks in the early growing season and derive useful rules of thumb around the environmental conditions during which canola crops are at a greater risk of turnip yellows virus transmission. [ABSTRACT FROM AUTHOR]

Background: A quantitative approach to predict expected muscle activity and mandibular movement from non‐invasive hard tissue assessments remains unexplored. Objectives: This study investigated the predictive potential of normalised muscle activity during various jaw movements combined with temporomandibular joint (TMJ) vibration analyses to predict expected maximum lateral deviation during mouth opening. Method: Sixty‐six participants underwent electrognathography (EGN), surface electromyography (EMG) and joint vibration analyses (JVA). They performed maximum mouth opening, lateral excursion and anterior protrusion as jaw movement activities in a single session. Multiple predictive models were trained from synthetic observations generated from the 66 human observations. Muscle function intensity and activity duration were normalised and a decision support system with branching logic was developed to predict lateral deviation. Performance of the models in predicting temporalis, masseter and digastric muscle activity from hard tissue data was evaluated through root mean squared error (RMSE) and mean absolute error. Results: Temporalis muscle intensity ranged from 0.135 ± 0.056, masseter from 0.111 ± 0.053 and digastric from 0.120 ± 0.051. Muscle activity duration varied with temporalis at 112.23 ± 126.81 ms, masseter at 101.02 ± 121.34 ms and digastric at 168.13 ± 222.82 ms. XGBoost predicted muscle intensity and activity duration and scored an RMSE of 0.03–0.05. Jaw deviations were successfully predicted with a MAE of 0.9 mm. Conclusion: Applying deep learning to EGN, EMG and JVA data can establish a quantifiable relationship between muscles and hard tissue movement within the TMJ complex and can predict jaw deviations. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to assess the effectiveness of the best performing interpretable machine learning models in the diagnoses of leukoplakia and oral squamous cell carcinoma (OSCC). Methods: A total of 237 patient cases were analysed that included information about patient demographics, lesion characteristics, and lifestyle factors, such as age, gender, tobacco use, and lesion size. The dataset was preprocessed and normalised, and then separated into training and testing sets. The following models were tested: K-Nearest Neighbours (KNN), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. The overall accuracy, Kappa score, class-specific precision, recall, and F1 score were used to assess performance. SHAP (SHapley Additive ExPlanations) was used to interpret the Random Forest model and determine the contribution of each feature to the predictions. Results: The Random Forest model had the best overall accuracy (93%) and Kappa score (0.90). For OSCC, it had a precision of 0.91, a recall of 1.00, and an F1 score of 0.95. The model had a precision of 1.00, recall of 0.78, and F1 score of 0.88 for leukoplakia without dysplasia. The precision for leukoplakia with dysplasia was 0.91, the recall was 1.00, and the F1 score was 0.95. The top three features influencing the prediction of leukoplakia with dysplasia are buccal mucosa localisation, ages greater than 60 years, and larger lesions. For leukoplakia without dysplasia, the key features are gingival localisation, larger lesions, and tongue localisation. In the case of OSCC, gingival localisation, floor-of-mouth localisation, and buccal mucosa localisation are the most influential features. Conclusions: The Random Forest model outperformed the other machine learning models in diagnosing oral cancer and potentially malignant oral lesions with higher accuracy and interpretability. The machine learning models struggled to identify dysplastic changes. Using SHAP improves the understanding of the importance of features, facilitating early diagnosis and possibly reducing mortality rates. The model notably indicated that lesions on the floor of the mouth were highly unlikely to be dysplastic, instead showing one of the highest probabilities for being OSCC. [ABSTRACT FROM AUTHOR]

As global population growth and urbanisation intensify energy demands, the quest for sustainable energy sources gains paramount importance. Hydrogen (H2) emerges as a versatile energy carrier, contributing to diverse processes in energy systems, industrial applications, and scientific research. To harness the H2 potential effectively, a profound grasp of its thermodynamic properties across varied conditions is essential. While field and laboratory measurements offer accuracy, they are resource-intensive. Experimentation involving high-pressure and high-temperature conditions poses risks, rendering precise H2 solubility determination crucial. This study evaluates the application of Deep Neural Networks (DNNs) for predicting H2 solubility in n-alkanes. Three DNNs are developed, focusing on model structure and overfitting mitigation. The investigation utilises a comprehensive dataset, employing distinct model structures. Our study successfully demonstrates that the incorporation of dropout layers and batch normalisation within DNNs significantly mitigates overfitting, resulting in robust and accurate predictions of H2 solubility in n-alkanes. The DNN models developed not only perform comparably to traditional ensemble methods but also offer greater stability across varying training conditions. These advancements are crucial for the safe and efficient design of H2-based systems, contributing directly to cleaner energy technologies. Understanding H2 solubility in hydrocarbons can enhance the efficiency of H2 storage and transportation, facilitating its integration into existing energy systems. This advancement supports the development of cleaner fuels and improves the overall sustainability of energy production, ultimately contributing to a reduction in reliance on fossil fuels and minimising the environmental impact of energy generation. [ABSTRACT FROM AUTHOR]

Abstract Background This paper demonstrates how available data can be explored and utilized to conclude generic patterns in the temporal changes in Human Milk (HM) composition. Methods The temporal trajectories of selected human milk components (HMC-s) were described, in the first four months postpartum, by a primary model consisting of two phases: a short linear phase in the colostrum, triggered by the parturition; and a longer second phase, where the concentration of the component converges to a steady state. The model was fitted to data available in a recently published database of temporal HMC trajectories both at the levels of individual molecules (such as specific fatty acid, oligosaccharide, and mineral molecules) and molecule-groups (such as total protein, total fat). Results The properties of the trajectories suggest that experimental designs should follow non-equidistant sampling times, with increasingly longer time intervals after the first week postpartum. A selected parameter, the final stationary level, of the primary model was then studied as a function of geographical location (secondary modelling). Conclusions We found that the total variation of the concentration of specific HMC-s is dominantly due to the inherent biological differences between individual mothers and to less extent to the geographical location.

Abstract Central serous chorioretinopathy (CSCR) is a retinal disease characterised by the accumulation of subretinal fluid, which often resolves spontaneously in acute cases. However, approximately one-third of patients experience recurrences that may cause severe and irreversible vision. This study aimed to identify parameters derived from optical coherence tomography (OCT) that are associated with CSCR recurrence. Our dataset included 5211 OCT scans from 344 eyes of 255 patients diagnosed with CSCR. 178 eyes were identified as recurrent, 109 as non-recurrent, and 57 were excluded. We extracted parameters using artificial intelligence algorithms based on U-Nets, convolutional kernels, and morphological operators. We applied inferential statistics to evaluate differences between the recurrent and non-recurrent groups, and we used a logistic regression predictive model, reporting the coefficients as a measure of biomarker importance. We identified nine predictive biomarkers for CSCR recurrence: age, intraretinal fluid, subretinal fluid, pigment epithelial detachments, choroidal vascularity index, integrity of photoreceptors and retinal pigment epithelium layer, choriocapillaris and choroidal stroma thickness, and thinning of the outer nuclear layer, and of the inner nuclear layer combined with the outer plexiform layer. These results could enable future developments in the automatic detection of CSCR recurrence, paving the way for translational medical applications.

Purpose: The purpose of this study is to assess the effectiveness of the best performing interpretable machine learning models in the diagnoses of leukoplakia and oral squamous cell carcinoma (OSCC). Methods: A total of 237 patient cases were analysed that included information about patient demographics, lesion characteristics, and lifestyle factors, such as age, gender, tobacco use, and lesion size. The dataset was preprocessed and normalised, and then separated into training and testing sets. The following models were tested: K-Nearest Neighbours (KNN), Logistic Regression, Naive Bayes, Support Vector Machine (SVM), and Random Forest. The overall accuracy, Kappa score, class-specific precision, recall, and F1 score were used to assess performance. SHAP (SHapley Additive ExPlanations) was used to interpret the Random Forest model and determine the contribution of each feature to the predictions. Results: The Random Forest model had the best overall accuracy (93%) and Kappa score (0.90). For OSCC, it had a precision of 0.91, a recall of 1.00, and an F1 score of 0.95. The model had a precision of 1.00, recall of 0.78, and F1 score of 0.88 for leukoplakia without dysplasia. The precision for leukoplakia with dysplasia was 0.91, the recall was 1.00, and the F1 score was 0.95. The top three features influencing the prediction of leukoplakia with dysplasia are buccal mucosa localisation, ages greater than 60 years, and larger lesions. For leukoplakia without dysplasia, the key features are gingival localisation, larger lesions, and tongue localisation. In the case of OSCC, gingival localisation, floor-of-mouth localisation, and buccal mucosa localisation are the most influential features. Conclusions: The Random Forest model outperformed the other machine learning models in diagnosing oral cancer and potentially malignant oral lesions with higher accuracy and interpretability. The machine learning models struggled to identify dysplastic changes. Using SHAP improves the understanding of the importance of features, facilitating early diagnosis and possibly reducing mortality rates. The model notably indicated that lesions on the floor of the mouth were highly unlikely to be dysplastic, instead showing one of the highest probabilities for being OSCC.

Abstract This study presents the findings of a comprehensive geotechnical and seismic site investigation conducted at Otuasega Town located in Bayelsa State within the Niger Delta region of Nigeria. Subsurface exploration involved advancing 10 boreholes to 30 m depth using hollow stem auger drilling. Continuous disturbed and undisturbed soil sampling was performed at 1.5 m intervals for detailed geotechnical testing. Laboratory tests on the recovered soil samples established the index properties, classification, densities and consistency limits of the stratified deposits. The subsurface profile comprised alternating layers of clay, silt and sand typical of deltaic sediments, with the clay fractions exhibiting medium to high plasticity. Shear wave velocity (Vs) profiling using Multichannel Analysis of Surface Waves (WASW) techniques categorised the site predominantly as Site Class C and D based on international standards. The Standard Penetration Test (SPT) N-values ranged from 5 to 10, indicating soft normally consolidated clay conditions typical of the Niger Delta region. Predictive empirical models developed from the field and lab data showed strong correlations for estimating key geotechnical parameters such as SPT blow count, Vs and liquefaction resistance. Ground response analyses using the Vs and SPT data indicated significant site amplification potential, with peak ground accelerations up to 1.5 times the bedrock motion. Liquefaction analysis based on the empirical SPT-based methods revealed a high potential for liquefaction in the sandy layers, especially under strong earthquake shaking. The study characterized the complex sedimentology and provided baseline information for seismic microzonation and site-specific ground response analyses to advance understanding of geohazards in this delta environment.

This study aimed to enhance the efficiency of by-product raw materials from the sugar industry for use as fuel. The approach involved developing an equation to calculate the higher heating value (HHV) for each type of raw material using a regression method. Additionally, a simplex-centroid mixture design (SCMD) was employed to estimate the lower heating value (LHV) based on the mixing ratios by weight of sugarcane trash leaves (SCL), sugarcane bagasse (SCB), and filter cake (FTC). The results demonstrated that the developed model accurately estimated the HHV for each raw material. The ultimate analysis showed high statistical appropriateness, with an R2 of 0.83. The standard error of estimation was 0.74 MJ/kg, and the mean absolute error was 0.76%. Furthermore, the SCMD effectively estimated the LHV of the SCL, SCB, and FTC mixture ratios, achieving an R2 of 99.77%. The evaluation and validation of the prediction equation revealed a mean absolute error of 7.57% and a mean bias error of 6.31%. The findings of this study can be used to enhance the combustion efficiency of sugar industry by-products for use as fuel by selecting the optimal mixing ratio for each type of raw material.

We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes. Ovid MEDLINE, CINAHL, Embase, Scopus, PubMed, and ClinicalTrials.gov were searched to identify randomised controlled trials (RCTs) evaluating the effectiveness of ML-driven interventions in surgical inpatient populations. The review was registered with PROSPERO (CRD42023433163) and conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analysis was conducted for outcomes with two or more studies using a random-effects model, and vote counting was conducted for other outcomes. Among 13 included RCTs, three types of ML-driven interventions were evaluated: Hypotension Prediction Index (HPI) (n =5), Nociception Level Index (NoL) (n =7), and a scheduling system (n =1). Compared with the standard care, HPI led to a significant decrease in absolute hypotension (n =421, P =0.003, I2=75%) and relative hypotension (n =208, P <0.0001, I2=0%); NoL led to significantly lower mean pain scores in the post-anaesthesia care unit (PACU) (n =191, P =0.004, I2=19%). NoL showed no significant impact on intraoperative opioid consumption (n =339, P =0.31, I2=92%) or PACU opioid consumption (n =339, P =0.11, I2=0%). No significant difference in hospital length of stay (n =361, P =0.81, I2=0%) and PACU stay (n =267, P =0.44, I2=0) was found between HPI and NoL. HPI decreased the duration of intraoperative hypotension, and NoL decreased postoperative pain scores, but no significant impact on other clinical outcomes was found. We highlight the need to address both methodological and clinical practice gaps to ensure the successful future implementation of ML-driven interventions. CRD42023433163 (PROSPERO). [ABSTRACT FROM AUTHOR]

The heat generated during metal cutting processes like end milling results in to higher temperatures which affects the quality of the surface produced. So, in order to get better surface quality, it is important to reduce the cutting zone temperatures. In the materials having high thermal conductivity the internal temperature rise of the workpiece also needs to be controlled. In this work, the study of the effects of controllable process parameters of CNC end milling on workpiece surface temperature rise, workpiece internal temperature rise and surface roughness during machining of aluminium alloy (Al 7075) was undertaken. The input parameters were spindle speed, feed rate, axial depth of cut, radial depth of cut and tool helix angle. The inclusion of helix angle as an input factor and, simultaneous reduction of workpiece surface temperature rise and workpiece internal temperature rise without compromising on the quality of the product were the major highlights of this work. This work also evaluates the trade-offs between heat generation and cutting quality. The central composite rotatable design was used for planning of the experiments and to develop predictive models. The direct and the interaction effects of the input parameters were studied and discussed. The optimization of process parameters was carried out by desirability analysis. The individual and composite desirability values were unity, which highlights, the successful implementation of the adopted methodology for achieving the desired goals. The optimum values of input parameters were: spindle speed: 4407 rpm, feed: 493 mm/min, axial depth of cut: 0.51 mm, radial depth of cut: 6.46 mm and helix angle: 30°. The results of the optimization were confirmed through the confirmation experiments with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Continuous growth of the manufacturing sector is resulting in to higher energy demand due to which the manufacturing costs and greenhouse gas emissions are also increasing. Beside reduction in energy consumption; improvement in energy efficiency, power factor and reduction in cutting temperatures are also vital to ensure better sustainability of the machining sector. This work evaluates the trade-offs between energy, heat generation and cutting quality during milling of medium carbon steel (EN8) alloy steel. The effects of input process parameters viz. spindle speed, feed rate, axial depth of cut, radial depth of cut and tool helix angle has been studied on the energy consumption, energy efficiency, power factor, cutting temperatures, surface roughness response parameters. The inclusion of helix angle as an input factor and, using energy efficiency and power factor as output parameters are the major highlights of this work. The machining experiments were conducted using response surface methodology for design of experiments. The multi objective optimization was carried out by using desirability approach, for three different groups of response variables considering the different importance of energy consumption, cutting temperatures and surface roughness, under different manufacturing circumstances. The predictability of the multiple regression approach was found to be more than 90% for all the responses which highlights model significance. The direct and interaction effect were studied and discussed in details for all the responses. The values of the composite desirability achieved in all the three types of optimization problems were on higher side (0.813, 1 and 0.794). The results of the optimization were confirmed by conducting the experiments the optimized settings. The percentage error between experimental and RSM predicted result was found to be within acceptable limits. This study can be helpful for reducing the energy consumption and cutting temperature without compromising on surface roughness, in the machining of medium carbon steel. [ABSTRACT FROM AUTHOR]

Abstract Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

Abstract Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.

Diapause is a vital survival strategy for insects, enabling them to conserve energy and endure adverse conditions. Understanding how diapause affects insect phenology and population dynamics is crucial for the effective management of insect pests. Predictive pest phenological models can be invaluable tools for providing essential information to support management strategies. This study presents a modelling framework to incorporate diapause into phenological models when biological information on variables regulating and functions describing diapause induction and termination are lacking or limited. In our framework, insect phenology is divided into a set of phases characterized by specific events (diapause induction and termination) and processes (development of diapausing and post‐diapausing biological stages). The phenology is simulated by a stage‐structured model based on the Kolmogorov equation, and the temperature‐dependent development rate functions are described by the Brière functional form. Our modelling framework was tested on a case study involving the prediction of the phenology of the codling moth, (Cydia pomonella L. 1758). Model calibration and validation were performed using four time‐series adult trap catch data collected in the Emilia Romagna Region from 2021 to 2023. The calibration procedure allowed obtaining realistic parameters related to the temperature threshold triggering diapause termination and the development rate function of post‐diapausing larvae and pupae. Model validation proved successful in simulating both the initial emergence and the overall phenological patterns of adults across the three observed generations. The methodological framework proposed here aims to facilitate the introduction of diapause in phenological models improving also their predictive abilities. The model may serve as an accurate and knowledge‐based tool for planning and implementing pest monitoring and control actions based on the realistic predictions provided by the model on the phenological status of the pest. [ABSTRACT FROM AUTHOR]

In this study, we evaluate predictive modelling techniques within project management, employing diverse architectures such as the LSTM, CNN, CNN-LSTM, GRU, MLP, and RNN models. The primary focus is on assessing the precision and consistency of predictions for crucial project parameters, including completion time, required personnel, and estimated costs. Our analysis utilises a comprehensive dataset that encapsulates the complexities inherent in real-world projects, providing a robust basis for evaluating model performance. The findings, presented through detailed tables and comparative charts, underscore the collective success of the models. The LSTM model stands out for its exceptional performance in consistently predicting completion time, personnel requirements, and estimated costs. Quantitative evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), corroborate the efficacy of the models. This study offers insights into the success observed, reflecting the potential for further refinement and continuous exploration to enhance the accuracy of predictive models in the ever-evolving landscape of project management. [ABSTRACT FROM AUTHOR]

The extensive use of polypropylene (PP) in various industries has heightened interest in developing efficient methods for recycling and optimising its mixtures. This study focuses on formulating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The investigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, attaining R2 values over 0.99. Symbolic regression further enhanced these models, offering significant improvements. To mitigate overfitting, empirical noise and variable swapping were introduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the development of recycling technologies and sustainable practices in the polymer industry by optimising processing and enhancing the use of recycled materials. [ABSTRACT FROM AUTHOR]

The aim of this research is to shed light on the complex interactions between player workload, traits, match-related factors, football performance, and injuries in the English Premier League. Using a range of statistical and machine learning techniques, this study analyzed a comprehensive dataset that included variables such as player workload, personal traits, and match statistics. The dataset comprises information on 532 players across 20 football clubs for the 2020–2021 English Premier League season. Key findings suggest that data, age, average minutes played per game, and club affiliations are significant indicators of both performance and injury incidence. The most effective model for predicting performance was Ridge Regression, whereas Extreme Gradient Boosting (XGBoost) was superior for predicting injuries. These insights are invaluable for data-driven decision-making in sports science and football teams, aiding in injury prevention and performance enhancement. The study's methodology and results have broad applications, extending beyond football to impact other areas of sports analytics and contributing to a flexible framework designed to enhance individual performance and fitness. [ABSTRACT FROM AUTHOR]

In today's dynamic business world, organizations are increasingly relying on innovative technologies to improve the efficiency and effectiveness of their human resource (HR) management. Our study uses historical time and attendance data collected with the MojeUre time and attendance system to predict employee absenteeism, including sick and vacation leave, using machine learning methods. We integrate employee demographic data and the absence profiles on timesheets showing daily attendance patterns as fundamental elements for our analysis. We also convert the absence data into a feature-based format suitable for the machine learning methods used. Our primary goal in this paper is to evaluate how well we can predict sick leave and vacation leave over short- and long-term intervals using tree-based machine learning methods based on the predictive clustering paradigm. This paper compares the effectiveness of these methods in different learning settings and discusses their impact on improving HR decision-making processes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) uses sophisticated algorithms to “learn” from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. Predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty‐four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID‐19. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated; implementation of this method could prove useful for BE operations. [ABSTRACT FROM AUTHOR]

Preserving the archaeological heritage of North Africa is challenging due to unique anthropic and environmental processes. This study introduces a Bayesian network approach to model and evaluates the impact of regional anthropogenic actions and environmental conditions on the gradual disappearance of endangered archaeological heritage in the Marmarica region (northeastern Libya/northwestern Egypt). The predictive model quantifies the complex relationship among human–environment interactions involved in such processes, specifically addressing the nonlinear vulnerability of archaeological heritage to the convergence of different threats, such as climate change, urban development, or natural erosion. This model also enables us to explore patterns of association between anthropic remains (e.g. settlements, productive places, mobility structures, etc.) and environmental factors (e.g. topography, climate, hydrology, etc.), considering different co-occurring agents in these dynamics. Furthermore, the model can be applied to study such processes in other regions, enabling wider effective risk management and heritage preservation efforts. [ABSTRACT FROM AUTHOR]

Background Older adults (≥65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimize ML models further. Accurately predicting hospitalization may tremendously affect the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient. Methods In this retrospective cohort study, a data set of 14 198 community-dwelling older adults (≥65 years) with complex care needs from the International Resident Assessment Instrument-Home Care database was used to develop and optimize 3 ML models to predict 30-day hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all 3 models to identify key predictors of 30-day hospitalization. Results The area under the receiver-operating characteristics curve for the RF, XGB, and LR models were 0.97, 0.90, and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day hospitalization. Conclusions Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting. [ABSTRACT FROM AUTHOR]

Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76–0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65–0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10–0.17) and negative predictive value of 0.98 (95% CI: 0.97–0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05–0.4. The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review. [ABSTRACT FROM AUTHOR]

The convergence of artificial intelligence (AI), big data (DB), and Internet of Things (IoT) in Society 5.0, has given rise to Marketing 5.0, revolutionizing personalized customer experiences. In this study, a systematic literature review was conducted to examine the integration of predictive modelling and sentiment analysis within the Marketing 5.0 domain. Unlike previous research, this study addresses both aspects within a single context, emphasizing the need for a sentiment-based predictive approach to the buyers' journey. This review explores how predictive and sentiment models enhance customer experience, inform business decisions, and optimize marketing processes. This study contributes to the literature by identifying areas of improvement in predictive modelling and emphasizes the role of a sentiment-based approach in Marketing 5.0. The sentiment-based model assists businesses in understanding customer preferences, offering personalized products, and enabling customers to receive relevant advertisements during their purchase journey. The paper's structure covers the evolution of traditional marketing to digital marketing, AI's role in digital marketing, predictive modelling in marketing, and the significance of analyzing customer sentiments in their reviews. The Prisma-P methodology, research questions, and suggestions for future work and limitations provide a comprehensive overview of the scope and contributions of this review. [ABSTRACT FROM AUTHOR]

The freshwater zooplankton of New Zealand-Aotearoa are species-poor with only two indigenous Daphnia species: D. thomsoni, and D. tewaipounamu. Over the last two decades, two species of invasive non-indigenous Daphnia, D. pulicaria/pulex and D. galeata, have become well established in many lakes in New Zealand-Aotearoa and their distribution appears to be rapidly increasing. We report the current distribution of these two invasive species in approximately 300 lakes throughout New Zealand-Aotearoa and test the hypothesis that the distribution and establishment of these invaders is more closely related to human access to a lake than to any environmental attributes of a lake's water or biota and consider potential implications of our findings for lake management. Boosted regression tree analysis identified total nitrogen and distance from the coast (D. pulicaria) and annual lake temperature (D. galeata) as key variables explaining current distributions. Inclusion of spatial autocorrelation overwhelmed other explanatory variables, highlighting the greater likelihood of spread from known source locations. Proximity to a road was an important predictor for D. pulicaria, and when tested separately both invasive Daphnia occurred significantly more often in lakes within 200 m of a road. Critically, D. galeata's realised niche completely envelopes D. thomsoni's, which could force the native species to become extinct through competitive exclusion. These results suggest that the spread of D. pulicaria and D. galeata to lakes where they do not yet occur is unlikely to be prevented unless public access to these waterways is denied or strictly controlled. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) has become a crucial aspect of maintaining the safety and durability of civil engineering structures, particularly concrete bridges. Traditional methods often rely on periodic inspections and manual sensor data analysis, which can be both time-intensive and susceptible to human error. With advancements in technology, Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Machine Learning (ML) algorithms have emerged as effective tools for predictive modeling in SHM. These AI models can process large amounts of real-time sensor data to predict key indicators like crack propagation, load-bearing capacity, and overall structural health. By providing accurate predictions, AI models enable more efficient maintenance scheduling, reducing both the risk of structural failure and unnecessary repair costs. This research explores various AI-driven models, comparing the performance of ANNs with other machine learning techniques such as Random Forest and Gradient Boosting. The findings demonstrate that AI can significantly improve the precision of SHM, offering a more scalable and cost-effective solution for infrastructure management, ultimately extending the service life of bridges while ensuring public. [ABSTRACT FROM AUTHOR]

Type 2 Diabetes (T2D) is a chronic metabolic disorder that affects millions of people worldwide, making early diagnosis crucial for effective treatment and management. Machine learning (ML) techniques have been increasingly utilized to enhance the classification accuracy of T2D by identifying complex patterns in patient data. This paper introduces a novel approach that leverages ensemble learning methods to improve the classification of T2D. By combining multiple classifiers and focusing on optimal feature selection, the proposed method achieves superior performance compared to traditional classification techniques. The effectiveness of the model was validated using multiple real-world healthcare datasets, demonstrating significant improvements in predictive accuracy. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) has become a significant cause of death around the world, with heart-related problems being a major contributor to this trend. It's crucial to identify the signs and symptoms of potential health problems before they become severe. To address this issue, an IoT-based prototype has been proposed. This prototype utilizes an ML-based prediction model to provide precautionary measures for heart-related issues. It includes an IoT device that gathers real-time data from the user's body, such as heart rate and ECG. This data is utilized to create an ML-based prediction model for heart-related problems. The model can alert the user of any potential heart-related risks. The proposed prototype is designed to offer a practical solution for the early detection of heart-related issues, therefore achieving a greater accuracy of 98.3% in lowering the death rate from heart disease than current methods. The results demonstrate that the suggested IoT-based prototype is useful for detecting various heart diseases and predicting future mortality rates compared to other existing machine-learning methods. [ABSTRACT FROM AUTHOR]